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README.md

README.md

IsoSeq™: PacBio® Isoform Sequencing.

The Isoform Sequencing (Iso-Seq™) Anaylsis refers to PacBio’s proprietary methods and software applications for transcriptome sequencing. The Iso-Seq application generates full-length cDNA sequences — from the 5’ end of transcripts to the poly-A tail — eliminating the need for transcriptome reconstruction using isoform-inference algorithms. The Iso-Seq method generates accurate information about alternatively spliced exons and transcriptional start sites. It also delivers information about poly-adenylation sites for transcripts up to 10 kb in length across the full complement of isoforms within targeted genes or the entire transcriptome.

This document describes the Iso-Seq software in SMRT® Analysis v3.0 release, which includes SMRT Link v1.0. Previous Iso-Seq documents can be found in its github wiki.

Table of contents

Overview

isoseq expanded

Analyses are performed in three stages, CCS, Classify and Cluster. Cluster employs the Iterative Clustering and Error correction (ICE) algorithm. For analyses performed on the command-line, there is an optional tool, Subset, for subsetting the IsoSeq results.

  • CCS
    • CCS is the first stage of an IsoSeq analysis. CCS builds circular consensus sequences (CCSs) from your subreads. More information about CCS is available here.
  • Classify
    • Classify is the second stage of an IsoSeq analysis. The key output of Classify is a file of full-length non-chimeric reads, and a file of non-full length reads. The key input of Classify is the circular consensus sequences generated from CCS. Classify will identify and remove polyA/T tails, remove primers, and identify read strandedness. Classify also removes artificial concatemers, but does not remove PCR chimeras.
  • Cluster
    • Cluster is the third stage of an IsoSeq analysis. The key outputs of Cluster is a file of polished, high-quality consensus sequences, and a file of polished, low-quality consensus sequences. The key input of clustering is the file of full-length non-chimeric reads, and a file of non-full length reads outputted by Classify.
  • Subset
    • Subset is an optional program which can be used to subset the output files for particular classes of sequences, such as non-chimeric reads, or non-full-length reads.

##Manual

There are three ways to run the Iso-Seq application: Using SMRT Link, on the command line, and on the command line using pbsmrtpipe so that you can run the whole Iso-Seq Analysis with one command given to pbsmrtpipe.

##Running with SMRT Link

To run the Iso-Seq application using SMRT Link, follow the usual steps for analysing data on SMRT Link. TODO: Link to document explaining SMRT Link.

##Running on the Command Line

On the command line, the analysis is performed in 3 steps:

  1. Run CCS on your subreads, generating a CCS BAM file. Then generate an XML from the BAM file.
  2. Run Classify on your CCSs with the XML as input, generating a FASTA of annotated sequences.
  3. Run Cluster on the FASTA produced by Classify, generating polished isoforms.

Step 1. CCS

First, convert your subreads to circular consensus sequences. You can do this with the command:

 ccs --noPolish --minLength=300 --minPasses=1 --minZScore=-999 --maxDropFraction=0.8 --minPredictedAccuracy=0.8 --minSnr=4 subreads.bam ccs.bam 

Where ccs.bam is where the CCSs will be output, and subreads.bam is the file containing your subreads. CCS options are described in pbccs doc. If you think that you have transcripts of interest that are less than 300 bp in length, be sure to adjust the minLength parameter. Next, you will generate an XML file from your CCSs. You can do this with the commmand:

 dataset create --type ConsensusReadSet ccs.xml ccs.bam

Where ccs.xml is the name of the XML file you are generating and ccs.bam is the name of the BAM file you generated previously using the ccs command.

Step 2. Classify

Classify can be run at the command line as follows:

 pbtranscript classify [OPTIONS] ccs.xml isoseq_draft.fasta --flnc=isoseq_flnc.fasta --nfl=isoseq_nfl.fasta

Where ccs.xml is the XML file you generated in Step 1.

Where isoseq_flnc.fasta contains only the full-length, non-chimeric reads.

And where isoseq_nfl.fasta contains all non-full-length reads.

Or you can run classify creating XML files instead of FASTA files as follows:

 pbtranscript classify [OPTIONS] ccs.xml isoseq_draft.fasta --flnc=isoseq_flnc.contigset.xml --nfl=isoseq_nfl.contigset.xml

Where ccs.xml is the XML file you generated in Step 1.

Where isoseq_flnc.contigset.xml contains only the full-length, non-chimeric reads.

And where isoseq_nfl.contigset.xml contains all non-full-length reads.

Note: One can always use pbtranscript subset to further subset isoseq_draft.fasta if --flnc and --nfl are not specified when you run pbtranscript classify. For example:

pbtranscript subset isoseq_draft.fasta isoseq_flnc.fasta --FL --nonChimeric

Step 3. Cluster and Polish

cluster can be run at the command line as follows:

 pbtranscript cluster [OPTIONS] isoseq_flnc.fasta polished_clustered.fasta --quiver --nfl=isoseq_nfl.fasta --bas_fofn=my.subreadset.xml

Or

 pbtranscript cluster [OPTIONS] isoseq_flnc.contigset.xml polished_clustered.contigset.xml --quiver --nfl=isoseq_nfl.contigset.xml --bas_fofn=my.subreadset.xml

Note: --quiver --nfl=isoseq_nfl.fasta|contigset.xml must be specified in order to get Quiver|Arrow polished consensus isoforms.

Optionally, you may call the following command to run ICE and create unpolished consensus isoforms only.

 pbtranscript cluster [OPTIONS] isoseq_flnc.fasta unpolished_clustered.fasta

##Running on the Command-Line with pbsmrtpipe ###Install pbsmrtpipe pbsmrtpipe is a part of smrtanalysis-3.0 package and will be installed if smrtanalysis-3.0 has been installed on your system. Or you can download pbsmrtpipe and install.

You can verify that pbsmrtpipe is running OK by:

pbsmrtpipe --help

Create a dataset

Now create an XML file from your subreads.

dataset create --type SubreadSet --generateIndices my.subreadset.xml subreads1.bam subreads2.bam ...

This will create a file called my.subreadset.xml.

Create and edit Iso-Seq Analysis options and global options for pbsmrtpipe.

Create a global options XML file which contains SGE related, job chunking and job distribution options that you may modify by:

 pbsmrtpipe show-workflow-options -o global_options.xml

Create an Iso-Seq options XML file which contains Iso-Seq related options that you may modify by:

 pbsmrtpipe show-template-details pbsmrtpipe.pipelines.sa3_ds_isoseq -o isoseq_options.xml

The entries in the options XML files have the format:

 <option id="pbtranscript.task_options.min_seq_len">
            <value>300</value>
        </option>

Note: If you only want to run Iso-Seq Classify without Cluster, please create an XML for Iso-Seq Classify Only.

 pbsmrtpipe show-template-details pbsmrtpipe.pipelines.sa3_ds_isoseq_classify -o isoseq_classify_options.xml

And you can modify options using your favorite text editor, such as vim.

Run the Iso-Seq application from pbsmrtpipe

Once you have set your options, you are ready to run the Iso-Seq software application via pbsmrtpipe:

pbsmrtpipe pipeline-id pbsmrtpipe.pipelines.sa3_ds_isoseq -e eid_subread:my.subreadset.xml --preset-xml=isoseq_options.xml --preset-xml=global_options.xml --output-dir=my_run

Where my_run is where the results of your analysis will be stored.

Advanced Analysis Options

SMRT Link/pbsmrtpipe Iso-Seq Options

You may modify Iso-Seq advanced analysis parameters for SMRT Link or pbsmrtpipe as follows.

Module Parameter pbsmrtpipe name Default Explanation
CCS Max. dropped fraction max_drop_fraction 0.08 Maximum fraction of subreads that can be dropped before giving up.
CCS Minimum length min_length 300 Sets a minimum length requirement for the median size of insert reads in order to generate a consensus sequence. If the targeted template is known to be a particular size range, this can filter out alternative DNA templates.
CCS Minimum Number of Passes min_passes 1 Sets a minimum number of passes for a ZMW to be emitted. This is the number of full passes. Full passes must have an adapter hit before and after the insert sequence and so does not include any partial passes at the start and end of the sequencing reaction. Additionally, the full pass count does not include any reads that were dropped by the Z-Filter.
CCS Minimum Predicted Accuracy min_predicted_accuracy 0.8 The minimum predicted accuracy of a read. CCS generates an accuracy prediction for each read, defined as the expected percentage of matches in an alignment of the consensus sequence to the true read. A value of 0.99 indicates that only reads expected to be 99% accurate are emitted.
CCS Minimum read score min_read_score 0.75 Minimum read score of input subreads.
CCS Minimum SNR min_snr 4 This filter removes data that is likely to contain deletions. SNR is a measure of the strength of signal for all 4 channels (A, C, G, T) used to detect basepair incorporation. The SNR can vary depending on where in the ZMW a SMRTbell™ template stochastically lands when loading occurs. SMRTbell templates that land near the edge and away from the center of the ZMW have a less intense signal, and as a result can contain sequences with more "missed" basepairs. This value sets the threshold for minimum required SNR for any of the four channels. Data with SNR < 3.75 is typically considered lower quality.
CCS Minimum Z Score min_zscore -9999 The minimum Z-Score for a subread to be included in the consensus generating process.
Classify Ignore polyA ignore_polya FALSE Full-Length criteria does not require polyA tail. By default this is off, which means that polyA tails are required for a sequence to be considered full length. When it is turned on, sequences do not need polyA tails to be considered full length.
Classify Min. seq. length min_seq_len 300 Minimum sequence length to output.
Cluster Minimum Quiver Arrow Accuracy hq_quiver_min_accuracy 0.99
Cluster-Polish Trim QVs 3' qv_trim_3p 30 Ignore QV of n bases in the 3' end.
Cluster-Polish Trim QVs 5' qv_trim_5p 100 Ignore QV of n bases in the 5' end.
Maximum Length (max_length) --maxLength=15000 Maximum length of subreads to use for generating CCS. Default = 7000
No Polish (no_polish) --noPolish Only output the initial template derived from the POA (faster, less accurate).

Note: The Iso-Seq Classify Only protocol does not perform isoform-level clustering and only uses a subset of advanced analysis parameters.

Classify Options

In order to display Classify advanced options via command line: pbtranscript classify --help.

Type Parameter Example Explanation
positional readsFN ccs.bam,xml,fasta First positional argument. It specifies input CCS reads in BAM, dataset XML, or FASTA format.
positional outReadsFN isoseq_draft.fasta,contigset.xml Second positional argument. Output file which contains all classified reads in FASTA or contigset XML format.
optional Help -h, --help This prints the help message.
optional Full-Length Non-Chimeric --flnc FLNC_FA.fasta,contigset.xml Outputs full-length non-chimeric reads in fasta or contigset XML format.
optional Output Non-Full-Length --nfl NFL_FA.fasta Outputs non-full-length reads in FASTA or contigset XML format.
HMMER HMMER Directory -d OUTDIR, --outDir OUTDIR Directory to store HMMER output (default: output/).
HMMER Summary -summary out.classify_summary.txt TXT file to output classify summary (default: out.classify_summary.txt).
HMMER Primers File -p primers.fa, --primer primers.fa Primer FASTA file (default: primers.fa).
HMMER Primers Report --report out.primer_info.csv CSV file of primer info. Contains the same info found in the description lines of the output FASTA (default: out.primer_info.csv).
HMMER CPUs --cpus CPUS Number of CPUs to run HMMER (default: 8).
Chimera-detection Minimum Sequence Length --min_seq_len MIN_SEQ_LEN Minimum CCS length to be analyzed. Fragments shorter than minimum sequence length are excluded from analysis (default: 300).
Chimera-detection Minimum PHMMER Score --min_score MIN_SCORE Minimum phmmer score for primer hit (default: 10).
Chimera-detection Non-Full-Length Chimeras --detect_chimera_nfl Detect chimeric reads among non-full-length reads. Non-full-length non-chimeric/chimeric reads will saved to outDir/nflnc.fasta and outDir/nflc.fasta.
Read-Extraction Ignore polyA --ignore_polyA Full-Length criteria does not require polyA tail. By default this is off, which means that polyA tails are required for a sequence to be considered full length. When it is turned on, sequences do not need polyA tails to be considered full length.

Cluster Options

In order to show Iso-Seq Cluster advanced options via command line: pbtranscript cluster.

Type Parameter Example Explanation
positional Input Reads isoseq_flnc.fasta,contigset.xml Input full-length non-chimeric reads in FASTA or contigset XML format. Used for clustering consensus isoforms.
positional Output Isoforms out.fasta,congitset.xml Output predicted (unpolished) consensus isoforms in FASTA file.
optional Help -h, --help This prints the help message.
optional Input Non-Full-Length --nfl_fa isoseq_nfl.fasta Input non-full-length reads in FASTA format, used for polishing consensus isoforms.
optional CCS QVs FOFN --ccs_fofn ccs.fofn A ccs.fofn or ccs.bam or ccs.xml file. If not given, Cluster assumes there is no QV information available.
optional Reads QVs FOFN --bas_fofn my.subreadset.xml A file which provides quality values of raw reads and subreads. Can be either a FOFN of BAM or BAX files, or a dataset XML.
optional Output Directory -d output/, --outDir output/ Directory to store temporary and output cluster files (default: output/).
optional Temp Directory --tmp_dir tmp/ Directory to store temporary files (default: tmp/).
optional Summary --summary my.cluster_summary.txt TXT file to output cluster summary (default: my.cluster_summary.txt).
optional Report --report report.csv A CSV file, each line containing a cluster, an associated read of the cluster, and the read type.
optional Pickle --pickle_fn PICKLE_FN Developers' option from which all clusters can be reconstructed.
ICE cDNA --cDNA_size {under1k,between1k2k,between2k3k,above3k} Estimated cDNA size.
ICE Quiver --quiver Call Quiver or Arrow to polish consensus isoforms using non-full-length non-chimeric CCS reads.
ICE Finer Quiver or Arrow --use_finer_qv Use finer classes of QV information from CCS input instead of a single QV from FASTQ. This option is slower and consumes more memory.
SGE Run SGE --use_sge Instructs Cluster to use SGE.
SGE Maximum SGE Jobs --max_sge_jobs MAX_SGE_JOBS The maximum number of jobs that will be submitted to SGE concurrently.
SGE SGE Job ID --unique_id UNIQUE_ID Unique ID for submitting SGE jobs.
SGE BLASR Cores --blasr_nproc BLASR_NPROC Number of cores for each BLASR job.
SGE Quiver or Arrow CPUs --quiver_nproc QUIVER_NPROC Number of CPUs each quiver or Arrow job uses.
IceQuiver High QV/Low QV Minimum Quiver or Arrow Accuracy --hq_quiver_min_accuracy HQ_QUIVER_MIN_ACCURACY Minimum allowed quiver or Arrow accuracy to classify an isoform as hiqh-quality.
IceQuiver High QV/Low QV Trim QVs 5' --qv_trim_5 QV_TRIM_5 Ignore QV of n bases in the 5' end.
IceQuiver High QV/Low QV Trim QVs 3' --qv_trim_3 QV_TRIM_3 Ignore QV of n bases in the 3' end.
IceQuiver High QV/Low QV High-Quality Isoforms FASTA --hq_isoforms_fa output/all_quivered_hq.fa Quiver or Arrow polished, high-quality isoforms in FASTA (default: output/all_quivered_hq.fa).
IceQuiver High QV/Low QV High-Quality Isoforms FASTQ --hq_isoforms_fq output/all_quivered_hq.fq Quiver or Arrow polished, high-quality isoforms in FASTQ (default: output/all_quivered_hq.fq).
IceQuiver High QV/Low QV Low-Quality Isoforms FASTA --lq_isoforms_fa output/all_quivered_lq.fa Quiver or Arrow polished, low-quality isoforms in FASTA (default: output/all_quivered_lq.fa).
IceQuiver High QV/Low QV Low-Quality Isoforms FASTQ --lq_isoforms_fq output/all_quivered_lq.fq Quiver or Arrow polished, low-quality isoforms in FASTQ (default: output/all_quivered_lq.fq).

Subset Options

In order to show pbtranscript Subset options via command line: pbtranscript subset.

Type Parameter Example Explanation
positional Input Sequences isoseq_draft.fasta Input FASTA file.
positional Output Sequences isoseq_subset.fasta Output FASTA file.
optional Help -h, --help This prints the help message.
optional Output Full-length --FL Reads to output must be Full-Length, with 3' primer and 5' primer and polyA tail seen.
optional Output Non-Full-length --nonFL Reads to output must be Non-Full-Length reads.
optional Output Non-Chimeric --nonChimeric Reads to output must be non-chimeric reads.
optional Output Read-Length --printReadLengthOnly Only print read lengths, no read names and sequences.
optional Ignore polyA Tails --ignore_polyA Full-Length criteria does not require polyA tail. By default this is off, which means that polyA tails are required for a sequence to be considered full length. When it is turned on, sequences do not need polyA tails to be considered full length.

Output Files

Classify Output Files

Classify FASTA Output (isoseq*.fasta)_

isoseq_flnc.fasta contains all full-length, non-artificial-concatemer reads.

isoseq_nfl.fasta contains all non-full-length reads.

isoseq_draft.fasta is an intermediate file in order to get full-length reads, which you can ignore.

Reads in these FASTA files look like the following:

>m140121_100730_42141_c100626750070000001823119808061462_s1_p0/119/30_1067_CCS strand=+;fiveseen=1;polyAseen=1;threeseen=1;fiveend=30;polyAend=1067;threeend=1096;primer=1;chimera=0
ATAAGACGACGCTATATG

These lines have the format:

<movie_name>/<ZMW>/<start>_<end>_CCS INFO

The info fields are:

  • strand: either + or -, whether a read is forward or reverse-complement cDNA,
  • fiveseen: whether or not 5' prime is seen in this read, 1 yes, 0 no
  • polyAseen: whether or not poly A tail is seen, 1 yes, 0 no
  • threeseen: whether or not 3' prime is seen, 1 yes, 0 no
  • fiveend: start position of 5'
  • threeend: start position of 3' in read
  • polyAend: start position of polyA in read
  • primer: index of primer seen in this read (remember primer fasta file >F0 xxxxx >R0 xxxxx >F1 xxxxx >R1 xxxx)
  • chimera: whether or not this read is classified as a chimeric cdna

Note: Reads in isoseq-flnc.fasta are always strand-specific. That is, the 5' and 3' primer (and sometimes the polyA tail) are used to tell whether the read is in the right strand. If needed, the scripts described here reverse-complement the original read and produce the sequence that is supposed to be the transcript. Non-full-length reads in isoseq_nfl.fasta on the other hand, could be in either orientation.

Summary (classify_summary.txt) This file contains the following statistics:

  • Number of reads of insert
  • Number of five prime reads
  • Number of three prime reads
  • Number of poly-A reads
  • Number of filtered short reads
  • Number of non-full-length reads
  • Number of full-length reads
  • Number of full-length non-chimeric reads
  • Average full-length non-chimeric read length

Note: By seeing that the number of full-length, non-chimeric (flnc) reads is only a little less than the number of full-length reads, we can confirm that the number of artificial concatemers is very low. This indicates a successful SMRTbell library prep.

##Cluster Output Files

Summary (cluster_summary.txt) This file contains the following statistics:

  • Number of consensus isoforms
  • Average read length of consensus isoforms

Report (cluster_report.csv) This is a csv file each line of which contains the following fields:

  • cluster_id: ID of a consensus isoforms from ICE.
  • read_id : ID of a read which supports the consensus isoform.
  • read_type : Type of the supportive read

Algorithm Modules

CCS

pbccs is a tool to create circular consensus sequences (CCS) sequence from raw subreads for PacBio sequences. See pbccs doc for usage.

Classify

Iso-Seq Classify classifies reads into full-length or non-full-length reads, artifical-concatemer chimeric or non-chimeric read.

In order to classify a read as full-length or non-full-length, we search for primers and polyA within reads. If and only if both primers and polyAs are seen in a read, we classify it as a full-length read. Otherwise, we classify the read as non-full-length. We also remove primers and polyAs from reads and identify read strandedness based on this information.

Note: The current version of the Iso-Seq application in SMRT Link 1.0 by default recognizes Clontech SMARTer primers.

Note: In SMRT Link 1.0, custom primers are NOT supported. In order to use custom primers, You must call pbtranscript from command line like

pbtranscript classify --primer your_primer_fasta ...

Where your_primer_fasta is a FASTA file with the following format:

    >F0
    5' sequence here
    >R0
    3' sequence here (but in reverse complement)

Next, we further look into full-length reads and classify them into artificial-concatemer chimeric reads or non-chimeric reads by locating primer hits within reads.

  • HMMER: We use phmmer in HMMER package to detect locations of primer hits within reads and classify reads which have primer hits in the middle of sequences as artificial-concatemer chimeric.

Cluster

Iso-Seq Cluster performs isoform-level clustering using the Iterative Clustering and Error correction (ICE) algorithm, which iteratively classifies full-length non-chimeric CCS reads into clusters and builds consensus sequences of clusters using pbdagcon.

ICE is customized to work well on alternative isoforms and alternative polyadenlynation sites, but not on SNP analysis and SNP based highly complex gene families.

For a detailed explanation of ICE, please refer to the Iso-Seq webinar recording and slides.

  • pbdagcon: pdagcon is a tool which builds consensus sequences using Directed Acyclic Graph Consensus.

Polish

Iso-Seq Polish further polishes consensus sequenecs of clusters (i.e., pbdagcon output) taking into account all the QV information. We assign not only full-length non-chimeric CCS reads but also non-full-length CCS reads into clusters based on similarity. Then for each cluster, we align raw subreads of its assigned ZMWs towards its consensus sequence. Finally, we load quality values to these alignments and polish the consensus sequence using Quiver or Arrow.

  • Quiver: Quiver|Arrow is a consensus and variant-calling algorithm for PacBio reads. Quiver or Arrow finds the maximum likelihood template sequence given PacBio reads of the template. It is used by the Iso-Seq application to polish consensus isoforms. Quiver|Arrow uses quality values and creates higher-quality consensus sequence comapred with pbdagcon, but is more time-consuming.

Diff SMRT Analysis v3.0 vs v2.3

The output of the Sequel™ instruments is BAM format, while the output of the PacBio RS and PacBio RS II instruments is bax.h5. Major differences between Iso-Seq software in SMRT Analysis v3.0 and Iso-Seq software in SMRT Analysis v2.3 are listed in the table below.

Note: Functions of Iso-Seq Analysis have NOT been changed since v2.3, and Iso-Seq Tofu has NOT been integrated.

Iso-Seq Application in SMRT Analysis v3.0 Iso-Seq Application in SMRT Analysis v2.3
SMRT Analysis Web Server: SMRT Link SMRT Analysis Web Server: SMRT Portal
Works on data from Sequel instrument Works on data from PacBio RS and RS II
Input PacBio reads are stored in BAM Input PacBio reads are stored in bax.h5 format
Supports PacBio DataSet Does NOT support PacBio Dataset
Uses new algorithm pbccs to create CCS reads Uses ConsensusTools.sh to create CCS reads
Does NOT support using customer primers from SMRT Link Supports using customer primers from SMRT Portal
Does NOT support using GMAP to align consensus isoforms to reference from SMRT Link Supports using GMAP to align consensus isoforms to reference from SMRT Portal
SMRT Link has two protocols: IsoSeq Classify Only and IsoSeq. The IsoSeq Classify Only protocol only classifies reads, while the IsoSeq protocol not only classifies reads but also generates consensus isoforms using ICE and polish them using Quiver or Arrow. SMRT Portal has one protocol: RS_IsoSeq, which provides options such that users can calssify reads, or run ICE and generate unpolished consensus isoforms or polish consensus isoforms using Quiver or Arrow.

##Handling PacBio RS and PacBio RS II data

If you want to run the Iso-Seq application on existing PacBio RS or PacBio RS II data, you will need to convert reads in bax.h5 files to BAM files.

Converting PacBio RS and PacBio RS II data to BAM with SMRT Link

TODO: points to SMRT Link Doc.

Converting PacBio RS and PacBio RS II data to BAM from command line

  bax2bam -o mynewbam mydata.1.bax.h5 mydata.2.bax.h5 mydata.3.bax.h5

Glossary

  • Chimera

    • Iso-Seq Classify classifies reads as artificial-concatemer chimeric or non-chimeric based on whether or not primers are found in the middle of the sequence.
  • High QV | Low QV

    • Iso-Seq Cluster generates polished consensus isoforms are classified into either high-quality or low-quality isoforms. We classify an isoform as high quality if its consensus accuracy is no less than a cut-off, otherwise low quality. The default cut-off is 0.99. You may change this value from command line, or via SMRT Link Advanced Analysis Parameters when creating an Iso-Seq job.

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